Disclaimer:
- This is a personnel project,
- I am not a employee of the state of Ohio.
- I am not employed in either the medical or data sceince fields.
Data Source: NY Times
## `summarise()` ungrouping output (override with `.groups` argument)
library(forecast)
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
buckeye$movavgCases <- ma(buckeye$daily_cases,order=7)
buckeye$movavgDeaths <- ma(buckeye$daily_deaths,order=7)
Ohio Totals for Cases, Deaths by Date
gg1 <- buckeye %>% ggplot() +
geom_line(aes(x=date,y=daily_cases,col="Cases")) +
geom_line(aes(x=date,y=movavgCases,col="7 Day Mov. Avg.")) +
labs(title="Ohio Cases by Date",y="Daily Cases (log10)") + scale_y_log10()
## NULL
## Warning: Transformation introduced infinite values in continuous y-axis
Ohio Accumulated Totals for Cases, Deaths by Date and County
buckeye %>% ggplot() +
geom_col(aes(x=date,y=Cases)) +
labs(title="Ohio Accumulated Cases by Date",x="Date",y="Accumulated Cases(log10)") +
scale_y_log10(labels = scales::comma) + geom_smooth(aes(x=date,y=Cases))
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

buckeye %>% ggplot() +
geom_col(aes(x=date,y=Deaths)) +
labs(title="Ohio Accumulated Deaths by Date",x="Date",y="Accumulated Cases(log10)") +
scale_y_log10(labels = scales::comma) + geom_smooth(aes(x=date,y=Deaths))
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (stat_smooth).
## Warning: Removed 11 rows containing missing values (geom_col).
